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1. 发明授权

US5544257A Continuous parameter hidden Markov model approach to automatic handwriting recognition 失效
标题翻译：连续参数隐马尔可夫模型法自动手写识别
公开(公告)号：US5544257A
公开(公告)日：1996-08-06
申请号：US818193
申请日：1992-01-08
申请人： Eveline J. Bellegarda , Jerome R. Bellegarda , David Nahamoo , Krishna S. Nathan
发明人： Eveline J. Bellegarda , Jerome R. Bellegarda , David Nahamoo , Krishna S. Nathan
IPC分类号： G06K9/62 , G06K9/68 , G06K9/70 , G06K9/00
CPC分类号： G06K9/6297
摘要： A computer-based system and method for recognizing handwriting. The present invention includes a preprocessor, a front end, and a modeling component. The present invention operates as follows. First, the present invention identifies the lexemes for all characters of interest. Second, the present invention performs a training phase in order to generate a hidden Markov model for each of the lexemes. Third, the present invention performs a decoding phase to recognize handwritten text. Hidden Markov models for lexemes are produced during the training phase. The present invention performs the decoding phase as follows. The present invention receives test characters to be decoded (that is, to be recognized). The present invention generates sequences of feature vectors for the test characters by mapping in chirographic space. For each of the test characters, the present invention computes probabilities that the test character can be generated by the hidden Markov models. The present invention decodes the test character as the recognized character associated with the hidden Markov model having the greatest probability.
摘要翻译：一种用于识别笔迹的基于计算机的系统和方法。本发明包括预处理器，前端和建模部件。本发明如下操作。首先，本发明识别所有感兴趣的人物的词汇。第二，本发明执行训练阶段，以便为每个词汇生成隐马尔可夫模型。第三，本发明执行解码阶段来识别手写文本。训练阶段产生了隐马尔可夫模型。本发明如下进行解码阶段。本发明接收要解码的测试字符（即将被识别）。本发明通过在手写空间中映射来生成用于测试字符的特征向量的序列。对于每个测试字符，本发明计算由隐马尔可夫模型可以产生测试字符的概率。本发明将测试字符解码为与具有最大概率的隐马尔可夫模型相关联的识别字符。

2. 发明授权

US5636291A Continuous parameter hidden Markov model approach to automatic handwriting recognition 失效
标题翻译：连续参数隐马尔可夫模型法自动手写识别
公开(公告)号：US5636291A
公开(公告)日：1997-06-03
申请号：US467615
申请日：1995-06-06
申请人： Eveline J. Bellegarda , Jerome R. Bellegarda , David Nahamoo , Krishna S. Nathan
发明人： Eveline J. Bellegarda , Jerome R. Bellegarda , David Nahamoo , Krishna S. Nathan
IPC分类号： G06K9/62 , G06K9/68 , G06K9/70 , G06K9/00 , G06F15/00
CPC分类号： G06K9/6297
摘要： A computer-based system and method for recognizing handwriting. The present invention includes a pre-processor, a front end, and a modeling component. The present invention operates as follows. First, the present invention identifies the lexemes for all characters of interest. Second, the present invention performs a training phase in order to generate a hidden Markov model for each of the lexemes. Third, the present invention performs a decoding phase to recognize handwritten text. Hidden Markov models for lexemes are produced during the training phase. The present invention performs the decoding phase as follows. The present invention receives test characters to be decoded (that is, to be recognized). The present invention generates sequences of feature vectors for the test characters by mapping in chirographic space. For each of the test characters, the present invention computes probabilities that the test character can be generated by the hidden Markov models. The present invention decodes the test character as the recognized character associated with the hidden Markov model having the greatest probability.
摘要翻译：一种用于识别笔迹的基于计算机的系统和方法。本发明包括预处理器，前端和建模组件。本发明如下操作。首先，本发明识别所有感兴趣的人物的词汇。第二，本发明执行训练阶段，以便为每个词汇生成隐马尔可夫模型。第三，本发明执行解码阶段来识别手写文本。训练阶段产生了隐马尔可夫模型。本发明如下进行解码阶段。本发明接收要解码的测试字符（即将被识别）。本发明通过在手写空间中映射来生成用于测试字符的特征向量的序列。对于每个测试字符，本发明计算由隐马尔可夫模型可以产生测试字符的概率。本发明将测试字符解码为与具有最大概率的隐马尔可夫模型相关联的识别字符。

3. 发明授权

US5343537A Statistical mixture approach to automatic handwriting recognition 失效
标题翻译：统计混合法自动手写识别
公开(公告)号：US5343537A
公开(公告)日：1994-08-30
申请号：US785642
申请日：1991-10-31
申请人： Eveline J. Bellegarda , Jerome R. Bellegarda , David Nahamoo , Krishna S. Nathan
发明人： Eveline J. Bellegarda , Jerome R. Bellegarda , David Nahamoo , Krishna S. Nathan
IPC分类号： G06K9/22 , G06K9/46 , G06K9/62 , G06K9/00
CPC分类号： G06K9/6217 , G06K9/00416 , G06K9/00429
摘要： Method and apparatus for automatic recognition of handwritten text based on a suitable representation of handwriting in one or several feature vector spaces(s), Gaussian modeling in each space, and mixture decoding to take into account the contribution of all relevant prototypes in all spaces. The feature vector space(s) is selected to encompass both a local and a global description of each appropriate point on a pen trajectory. Windowing is performed to capture broad trends in the handwriting, after which a linear transformation is applied to suitably eliminate redundancy. The resulting feature vector space(s) is called chirographic space(s). Gaussian modeling is performed to isolate adequate chirographic prototype distributions in each space, and the mixture coefficients weighting these distributions are trained using a maximum likelihood framework. Decoding can be performed simply and effectively by accumulating the contribution of all relevant prototype distributions. Post-processing using a language model may be included.
摘要翻译：基于在一个或多个特征向量空间中的手写的适当表示，每个空间中的高斯建模，以及混合解码，以便考虑所有空间中所有相关原型的贡献，自动识别手写文本的方法和装置。选择特征向量空间以包含笔轨迹上的每个适当点的局部和全局描述。执行窗口以捕获手写的广泛趋势，之后应用线性变换以适当地消除冗余。所得到的特征向量空间称为手绘空间。执行高斯建模以分离每个空间中的足够的手写原型分布，并且使用最大似然框架训练对这些分布加权的混合系数。通过积累所有相关原型分布的贡献，可以简单有效地执行解码。可以包括使用语言模型的后处理。

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